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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Luo, Chang Li, Yan Yang, Tong You, Jin Xu, Huiqin School of Control Science and Engineering Shandong University Jinan250061 China

ISBN: (纸本)9798350361674

The state of charge (SOC) estimation is essential for battery management systems (BMS), necessitating proper modeling and filtering approaches. This study zeroes in on enhancing the accuracy of SOC estimation of battery thanks to an appropriate modeling technique. Considering comprehensive criteria for model structure identification (AIC, BIC, CIC), a second-order RC equivalent circuit model has been chosen. Whose estimation of parameters is transformed into a nonlinear optimization challenge. The cuckoo search algorithm, acclaimed for multi-objective optimization, is employed with a well-defined fitness function, facilitating the determination of optimized parameters. Subsequently, in view of real-world applications, the extended Kalman filter (EKF) algorithm is applied to estimate the SOC with the above optimized second order model. Experimental data analysis affirms that, with appropriate dimensions and iterations, this algorithm efficiently and accurately solves for modeling and SOC estimation of lithium-ion batteries, as well as bolstering its advantages in BMS applications. © 2024 ieee.

关键词： State of charge

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data-driven Strategy Synthesis for Stochastic systems with Unknown Nonlinear Disturbances 6

Data-Driven Strategy Synthesis for Stochastic Systems with U...

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6th Annual learning for Dynamics and control conference

作者： Gracia, Ibon Boskos, Dimitris Laurenti, Luca Lahijanian, Morteza Univ Colorado Boulder Boulder CO 80309 USA Delft Univ Technol Delft Netherlands

In this paper, we introduce a data-driven framework for synthesis of provably-correct controllers for general nonlinear switched systems under complex specifications. The focus is on systems with unknown disturbances whose effects on the dynamics of the system is nonlinear. The specification is assumed to be given as linear temporal logic over finite traces (LTLf) formulas. Starting from observations of either the disturbance or the state of the system, we first learn an ambiguity set that contains the unknown distribution of the disturbances with a user-defined confidence. Next, we construct a robust Markov decision process (RMDP) as a finite abstraction of the system. By composing the RMDP with the automaton obtained from the LTLf formula and performing optimal robust value iteration on the composed RMDP, we synthesize a strategy that yields a high probability that the uncertain system satisfies the specifications. Our empirical evaluations on systems with a wide variety of disturbances show that the strategies synthesized with our approach lead to high satisfaction probabilities and validate the theoretical guarantees.

关键词： Formal synthesis Switched stochastic systems Robust Markov decision processes Wasserstein distance data-driven control Nonlinear control Nonlinear disturbances

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data-driven Fault Detection for Lithium-Ion Battery Packs via Behavioral System Representation

Data-Driven Fault Detection for Lithium-Ion Battery Packs vi...

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ieee conference on control Technology and Applications (CCTA)

作者： Jin, Hailang Wang, Yibo Huang, Dexian Shang, Chao Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

ISBN: (纸本)9798350370959;9798350370942

Fault detection and diagnosis of lithium-ion batteries have been of intense investigation in energy systems, but most applicable methods rely on precise and complicated mechanistic models, which are nontrivial to establish in practice. The recently emerging behavioral system theory yields a new model-free representation of dynamical systems using only a single input-output trajectory. This enables us to develop a new data-driven fault detection scheme for lithium-ion battery packs, which effectively captures battery dynamics from data and bypasses the cumbersome work of building and calibrating first-principle battery models. By applying Willems' fundamental lemma to the innovation form of stochastic systems, our method attains a purely data-driven yet approximated realization of Kalman filter-based output predictor, which inspires a new data-driven residual generator for fault detection. Compared with generic data-driven residual generators such as stable kernel representation, our method performs more closely to a Kalman filter-based residual generator and thus better handles uncertainty including stochastic disturbance and noise. Simulations on a three-cell battery pack demonstrate the effectiveness of our method and its outperformance over existing data-driven fault detection design.

关键词： Stochastic systems

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Exploration-enhanced multi-agent reinforcement learning for distributed PV-ESS scheduling with incomplete data

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APPLIED ENERGY 2024年 359卷

作者： Li, Yutong Houb, Jian Yan, Gangfeng Zhejiang Univ Coll Elect Engn Hangzhou Peoples R China Zhejiang Sci Tech Univ Sch Informat Sci & Technol Hangzhou Peoples R China

This paper investigates the scheduling problem in smart distribution networks equipped with distributed photovoltaic energy storage systems (PV-ESS) to address excessive power losses, economic revenue, and overvoltage issues. Accurately modeling the grid structure and ensuring adequate sensor coverage pose significant challenges in network settings of this nature, and therefore, we put forth a novel approach known as Principal Component Analysis -based incomplete data equivalence (PIDE) for constructing a data -driven power flow model under incomplete data. Moreover, the presence of distributed PV-ESS, coupled with the lack of data sharing, introduces a hybrid cooperation -competition dynamic, resulting in suboptimal solutions and local optima. To address this challenge, we approach the scheduling problem by formulating it as a multi -agent reinforcement learning task. Meanwhile, we present Counterfactual Multi -agent Soft Actor-Critic (COSAC), which incorporates stochastic policy learning to enhance exploration and facilitates credit assignment in the continuous action space, so as to accurately determine the individual contributions of agents involved in the task. Simulation results conducted on the ieee 33 and 123 bus systems demonstrate the effectiveness of the proposed method. Specifically, we find that PIDE achieves a substantial reduction in the necessary data sampling coverage, and COSAC outperforms state-of-the-art multi -agent reinforcement learning methods by at least 4.14%.

关键词： data-driven data dimension reduction Multi-agent reinforcement learning Energy storage system

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data-driven Output Regulation using Single-Gain Tuning Regulators 62

Data-Driven Output Regulation using Single-Gain Tuning Regul...

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62nd ieee conference on Decision and control (CDC)

作者： Chen, Liangjie Simpson-Porco, John W. Univ Toronto Dept Elect & Comp Engn 10 Kings Coll Rd Toronto ON M5S 3G4 Canada

ISBN: (纸本)9798350301243

Current approaches to data-driven control are geared towards optimal performance, and often integrate aspects of machine learning and large-scale convex optimization, leading to complex implementations. In many applications, it may be preferable to sacrifice performance to obtain significantly simpler controller designs. We focus here on the problem of output regulation for linear systems, and revisit the so-called tuning regulator of E. J. Davison as a minimal-order data-driven design for tracking and disturbance rejection. Our proposed modification of the tuning regulator relies only on samples of the open-loop plant frequency response for design, is tuned online by adjusting a single scalar gain, and comes with a guaranteed margin of stability;this provides a faithful extension of tuning procedures for SISO integral controllers to MIMO systems with mixed constant and harmonic disturbances. The results are illustrated via application to a four-tank water control process.

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Deep Residual learning for Channel Estimation in UAV mmWave systems: A Model-driven Approach

Deep Residual Learning for Channel Estimation in UAV mmWave ...

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ieee/CIC International conference on Communications in China (ICCC)

作者： Xie, Daixin Liu, Chenxi Wang, Wei Hu, Xiaoling Peng, Mugen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Peng Cheng Lab Shenzhen 518000 Peoples R China

ISBN: (纸本)9798350378412

Accurate channel estimation is pivotal in satisfying the requirement of ultra-high data rates in future wireless networks. In this paper, we propose a novel model-driven channel estimation algorithm for an unmanned aerial vehicle (UAV) millimeter wave (mmWave) communication system, in which a UAV equipped with a large-scale antenna array performs the channel estimation based on the pilot signals from another UAV equipped with a single antenna. Considering the sparseness of the UAV mmWave channels, in the proposed algorithm, we exploit the use of deep residual learning to judiciously learn the key parameters of the alternating direction method of multipliers (ADMM) framework, reformulating the classical model-based channel estimation algorithm into deep learning model-based model-driven channel estimator. Compared to various benchmark schemes, we validate the efficiency of our suggested model-based channel estimation algorithm, and show how it can achieve good channel estimation accuracy even when the link quality is moderate and the pilot resources are limited.

关键词： Channel estimation deep residual learning model-driven UAV mmWave systems

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SFS-PSO: An Improved data Preprocessing Approach in Fault Diagnosis under Variable Working Conditions 13

SFS-PSO: An Improved Data Preprocessing Approach in Fault Di...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Zhang, Fanglue Yang, Rui School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou215123 China School of Electrical Engineering Electronics and Computer Science University of Liverpool LiverpoolL69 3BX United Kingdom

ISBN: (纸本)9798350361674

This paper presents an approach for bearing fault diagnosis that leverages sensitive feature selection to tackle the challenge of high intra-class distances and low inter-class distances in data pre-processing of conventional deep transfer learning methods under variable working conditions. Initially, bearing fault signals are transformed from time to frequency domain using the Fast Fourier Transform. Then, these frequency-domain features are refined through a selection process optimized by particle swarm optimization, focusing on those with higher sensitivity weights to reduce intra-class distance and enhance inter-class distance. These selected features are input into domain adversarial neural networks for accurate bearing fault diagnosis under different conditions. Experimental results demonstrate the method's effectiveness and increased diagnostic accuracy. © 2024 ieee.

关键词： Frequency domain analysis

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D?K? I: data-Knowledge-driven Group Intelligence Framework for Smart Service in Education Metaverse

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ieee TRANSACTIONS ON systems MAN CYBERNETICS-systems 2023年第4期53卷 2056-2061页

作者： Zheng, Wenbo Yan, Lan Zhang, Wenwen Ouyang, Liwei Wen, Ding Wuhan Univ Technol Sch Comp Sci & Artificial Intelligence Wuhan 430070 Peoples R China Wuhan Univ Technol Sanya Sci & Educ Innovat Pk Sanya 572000 Peoples R China Cent China Normal Univ Sch Comp Sci Wuhan 430070 Peoples R China Hunan Univ Coll Informat Sci & Engn Changsha 410082 Peoples R China Hunan Univ Natl Supercomp Ctr Changsha 410082 Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian 710119 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China North Automat Control Technol Inst Taiyuan Peoples R China

Metaverse is the fusion of cyber-physical-social intelligence, and the fusion becomes the core and fundamental property of the metaverse. As an important part of social operationalization, the education domain leads to the birth of the education metaverse. This article answers three basic questions about smart services in the education metaverse: 1) learning scene;2) technical framework;and 3) initial expansion. Specifically, four key elements constitute the learning scene in the education metaverse: 1) the learner;2) its time;3) space;and 4) learning event. In this learning scene, we propose a novel data-knowledge-driven group intelligence framework, aiming to transform data in the education metaverse into knowledge, and intersect and integrate intelligence with knowledge;based on this framework, we apply it to specific services, i.e., transaction and management services. We hope that our work opens the door to research on smart services in the education metaverse and more scholars will work for these challenges.

关键词： Education Artificial intelligence Artificial intelligence (AI) data education metaverse fusion knowledge

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Everyone in control, Everywhere

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ieee control systems MAGAZINE 2023年第1期43卷 4-7页

作者： Sepulchre, Rodolphe

The theme of this month's issue of ieee control systems is "Everyone in control, Everywhere." The magazine presents two features and one control education article. The first feature is an example-driven tutorial introduction to quantum control. It is the product of a three-year multidisciplinary collaboration between a team of control engineers and a team of quantum scientists: Marco M. Nicotra, Jieqiu Shao, Joshua Combes, Anne Cross Theurkauf, Penina Axelrad, Liang-Ying Chih, Murray Holland, Alex A. Zozulya, Catie K. LeDesma, Kendall Mehling, and Dana Z. Anderson. In the author's experiences, the greatest challenge one faces when entering the field of quantum control is the language barrier between the two communities. The aim of this article is to lower this barrier by showing how familiar control strategies (that is, Lyapunov, optimal control, and learning) can be applied in the unfamiliar setting of a quantum system (that is, a cloud of trapped, ultracold atoms). Particular emphasis is given to the derivation of the model and the description of its structural properties. Sidebars throughout the article prove a brief overview of the essential notions/notation that are/is required to establish an effective communication channel with quantum physicists and quantum engineers. In essence, this article is a collection of everything that this control team wished they had known at the beginning of the project. They hope that it may be of assistance to members of this community wanting to embark on their first quantum control project. The second feature proposes a model-free deep reinforcement learning strategy for shared control of robot manipulators with obstacle avoidance. It is coauthored by Matteo Rubagotti, Bianca Sangiovanni, Aigerim Nurbayeva, Gian Paolo Incremona, Antonella Ferrara, and Almas Shintemirov. The proposed strategy is tested in simulation and experimentally on a UR5 manipulator, and it is compared with a model predictive control approach. The article

关键词： Model predictive control

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data-driven Trajectory Tracking control for Nonlinear Low-altitude Vehicles 7

Data-driven Trajectory Tracking Control for Nonlinear Low-al...

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7th International conference on Automation Electronics and Electrical Engineering

作者： Yang, Baoshuo Xidian Univ Xian Shaanxi Peoples R China

ISBN: (纸本)9798350377040;9798350377033

This paper addresses the trajectory tracking control problem for nonlinear low-altitude vehicles by proposing a data-driven, model-free adaptive control scheme to circumvent the complexities and challenges associated with traditional modeling. To reduce tracking errors for unmanned aerial vehicles (UAVs), a parameter dynamic updating mechanism is introduced. Based on data-driven techniques and parameter updates, a novel adaptive flight control algorithm is presented. Then, simulation examples are provided to demonstrate the effectiveness and stability of the proposed algorithm by incorporating three types of communication interruptions and compensations to analyze their impacts on the system. Finally, parameter tuning is conducted through PID comparison experiments.

关键词： Trajectory Tracking Nonlinear systems data-driven Dynamic Linearization

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